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Robust Beamforming Based on Complex-Valued Convolutional Neural Networks for Sensor Arrays

  • Robust adaptive beamforming (RAB) plays a vital role in modern communications by ensuring the reception of high-quality signals. This article proposes a deep learning approach to robust adaptive beamforming. In particular, we propose a novel RAB approach where the sample covariance matrix (SCM) is used as the input of a deep 1D Complex-Valued Convolutional Neural Network (CVCNN). The network employs complex convolutional and pooling layers, as well as a Cartesian Scaled Exponential Linear Unit activation function to directly compute the nearly-optimum weight vector through the training process and without prior knowledge about the direction of arrival of the desired signal. This means that reconstruction of the interference plus noise (IPN) covariance matrix is not required. The trained CVCNN accurately computes the nearly-optimum weight vector for data not used during training. The computed weight vector is employed to estimate the signal-to-interference plus noise ratio. Simulations show that the proposed RAB can provide performance close to that of the optimal beamformer.
Author:Saeed Mohammadzadeh, Vítor H. Nascimento, Rodrigo C. de Lamare, Noushin Hajarolasvadi
Document Type:Article
Parent Title (English):IEEE Signal Processing Letters
First Page:2018
Last Page:2021
Year of first publication:2022
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